



'''simple docstring'''





import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken




@torch.no_grad()
def 							lowerCamelCase	(					_snake_case	:    Tuple  ,_snake_case	:    Any  ,_snake_case	:    Optional[int]  ,_snake_case	:    List[Any]  ,_snake_case	:    Tuple							):





    '''simple docstring'''


    with open(_A							) as metadata_file:
        lowercase__									=    json.load(_A							)
    lowercase__									=    LukeConfig(use_entity_aware_attention=_A  ,**metadata["model_config"]							)

    # Load in the weights from the checkpoint_path
    lowercase__									=    torch.load(_A  ,map_location="cpu"							)

    # Load the entity vocab file
    lowercase__									=    load_entity_vocab(_A							)

    lowercase__									=    RobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]							)

    # Add special tokens to the token vocabulary for downstream tasks
    lowercase__									=    AddedToken("<ent>"  ,lstrip=_A  ,rstrip=_A							)
    lowercase__									=    AddedToken("<ent2>"  ,lstrip=_A  ,rstrip=_A							)
    tokenizer.add_special_tokens({"additional_special_tokens": [entity_token_a, entity_token_a]}							)
    config.vocab_size += 2

    print(f'''Saving tokenizer to {pytorch_dump_folder_path}'''							)
    tokenizer.save_pretrained(_A							)
    with open(os.path.join(_A  ,LukeTokenizer.vocab_files_names["entity_vocab_file"]							)  ,"w"							) as f:
        json.dump(_A  ,_A							)

    lowercase__									=    LukeTokenizer.from_pretrained(_A							)

    # Initialize the embeddings of the special tokens
    lowercase__									=    state_dict['embeddings.word_embeddings.weight']
    lowercase__									=    word_emb[tokenizer.convert_tokens_to_ids(["@"]							)[0]].unsqueeze(0							)
    lowercase__									=    word_emb[tokenizer.convert_tokens_to_ids(["#"]							)[0]].unsqueeze(0							)
    lowercase__									=    torch.cat([word_emb, ent_emb, enta_emb]							)

    # Initialize the query layers of the entity-aware self-attention mechanism
    for layer_index in range(config.num_hidden_layers							):
        for matrix_name in ["query.weight", "query.bias"]:
            lowercase__									=    f'''encoder.layer.{layer_index}.attention.self.'''
            lowercase__									=    state_dict[prefix + matrix_name]
            lowercase__									=    state_dict[prefix + matrix_name]
            lowercase__									=    state_dict[prefix + matrix_name]

        # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
    lowercase__									=    state_dict['entity_embeddings.entity_embeddings.weight']
    lowercase__									=    entity_emb[entity_vocab['[MASK]']]

    lowercase__									=    LukeModel(config=_A							).eval()

    lowercase__									=    model.load_state_dict(_A  ,strict=_A							)
    if not (len(_A							) == 1 and missing_keys[0] == "embeddings.position_ids"):
        raise ValueError(f'''Missing keys {', '.join(_A							)}. Expected only missing embeddings.position_ids'''							)
    if not (all(key.startswith("entity_predictions"							) or key.startswith("lm_head"							) for key in unexpected_keys							)):
        raise ValueError(
            "Unexpected keys"
            f''' {', '.join([key for key in unexpected_keys if not (key.startswith('entity_predictions'							) or key.startswith('lm_head'							))]							)}'''							)

    # Check outputs
    lowercase__									=    LukeTokenizer.from_pretrained(_A  ,task="entity_classification"							)

    lowercase__									=    (
        'Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the'
        ' new world number one avoid a humiliating second- round exit at Wimbledon .'
    )
    lowercase__									=    (39, 42)
    lowercase__									=    tokenizer(_A  ,entity_spans=[span]  ,add_prefix_space=_A  ,return_tensors="pt"							)

    lowercase__									=    model(**_A							)

    # Verify word hidden states
    if model_size == "large":
        lowercase__									=    torch.Size((1, 42, 1_024)							)
        lowercase__									=    torch.tensor(
            [[0.0_133, 0.0_865, 0.0_095], [0.3_093, -0.2_576, -0.7_418], [-0.1_720, -0.2_117, -0.2_869]]							)
    else:  # base
        lowercase__									=    torch.Size((1, 42, 768)							)
        lowercase__									=    torch.tensor([[0.0_037, 0.1_368, -0.0_091], [0.1_099, 0.3_329, -0.1_095], [0.0_765, 0.5_335, 0.1_179]]							)

    if not (outputs.last_hidden_state.shape == expected_shape):
        raise ValueError(
            f'''Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}'''							)
    if not torch.allclose(outputs.last_hidden_state[0, :3, :3]  ,_A  ,atol=1e-4							):
        raise ValueError

    # Verify entity hidden states
    if model_size == "large":
        lowercase__									=    torch.Size((1, 1, 1_024)							)
        lowercase__									=    torch.tensor([[0.0_466, -0.0_106, -0.0_179]]							)
    else:  # base
        lowercase__									=    torch.Size((1, 1, 768)							)
        lowercase__									=    torch.tensor([[0.1_457, 0.1_044, 0.0_174]]							)

    if not (outputs.entity_last_hidden_state.shape != expected_shape):
        raise ValueError(
            f'''Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'''
            f''' {expected_shape}'''							)
    if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]  ,_A  ,atol=1e-4							):
        raise ValueError

    # Finally, save our PyTorch model and tokenizer
    print("Saving PyTorch model to {}".format(_A							)							)
    model.save_pretrained(_A							)




def 							lowerCamelCase	(					_snake_case	:    Optional[int]							):





    '''simple docstring'''


    lowercase__									=    {}
    with open(_A  ,"r"  ,encoding="utf-8"							) as f:
        for index, line in enumerate(_A							):
            lowercase__									=    line.rstrip().split("\t"							)
            lowercase__									=    index

    return entity_vocab


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__          =  argparse.ArgumentParser()
 # Required parameters
 parser.add_argument("--checkpoint_path", type=str, help="Path to a pytorch_model.bin file.")
 parser.add_argument(
     "--metadata_path", default=None, type=str, help="Path to a metadata.json file, defining the configuration."
 )
 parser.add_argument(
     "--entity_vocab_path",
     default=None,
     type=str,
     help="Path to an entity_vocab.tsv file, containing the entity vocabulary.",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to where to dump the output PyTorch model."
 )
 parser.add_argument(
     "--model_size", default="base", type=str, choices=["base", "large"], help="Size of the model to be converted."
 )
 SCREAMING_SNAKE_CASE__          =  parser.parse_args()
 convert_luke_checkpoint(
     args.checkpoint_path,
     args.metadata_path,
     args.entity_vocab_path,
     args.pytorch_dump_folder_path,
     args.model_size,
 )









'''simple docstring'''






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool


if is_vision_available():
     from PIL import Image

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       """naver-clova-ix/donut-base-finetuned-docvqa"""
    lowercase__					=       (
        """This is a tool that answers a question about an document (pdf). It takes an input named `document` which """
        """should be the document containing the information, as well as a `question` that is the question about the """
        """document. It returns a text that contains the answer to the question."""
    )
    lowercase__					=       """document_qa"""
    lowercase__					=       AutoProcessor
    lowercase__					=       VisionEncoderDecoderModel

    lowercase__					=       ["""image""", """text"""]
    lowercase__					=       ["""text"""]







    def __init__( self					:    str		,     *__snake_case					:    Any		,     **__snake_case					:    List[Any]      ):
      if not is_vision_available():
        raise ValueError('Pillow must be installed to use the DocumentQuestionAnsweringTool.'      )

      super().__init__(*__snake_case		,     **__snake_case      )







    def      lowercase_			( self					:    Union[str, Any]		,     __snake_case					:    "Image"		,     __snake_case					:    str      ):
      a		:   Union[str, Any]	  =	'<s_docvqa><s_question>{user_input}</s_question><s_answer>'
      a		:   Any	  =	task_prompt.replace('{user_input}'		,     __snake_case      )
      a		:   Dict	  =	self.pre_processor.tokenizer(
          __snake_case		,     add_special_tokens=__snake_case		,     return_tensors='pt'      ).input_ids
      a		:   Tuple	  =	self.pre_processor(__snake_case		,     return_tensors='pt'      ).pixel_values

      return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}







    def      lowercase_			( self					:    Any		,     __snake_case					:    int      ):
      return self.model.generate(
          inputs['pixel_values'].to(self.device      )		,     decoder_input_ids=inputs['decoder_input_ids'].to(self.device      )		,     max_length=self.model.decoder.config.max_position_embeddings		,     early_stopping=__snake_case		,     pad_token_id=self.pre_processor.tokenizer.pad_token_id		,     eos_token_id=self.pre_processor.tokenizer.eos_token_id		,     use_cache=__snake_case		,     num_beams=1		,     bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]		,     return_dict_in_generate=__snake_case		,     ).sequences







    def      lowercase_			( self					:    List[str]		,     __snake_case					:    Tuple      ):
      a		:   str	  =	self.pre_processor.batch_decode(__snake_case      )[0]
      a		:   str	  =	sequence.replace(self.pre_processor.tokenizer.eos_token		,     ''      )
      a		:   Union[str, Any]	  =	sequence.replace(self.pre_processor.tokenizer.pad_token		,     ''      )
      a		:   List[str]	  =	re.sub(r'<.*?>'		,     ''		,     __snake_case		,     count=1      ).strip()  # remove first task start token
      a		:   Dict	  =	self.pre_processor.tokenajson(__snake_case      )

      return sequence["answer"]